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Maximisation of mutual information for gait-based soft biometric classification using Gabor features

Maximisation of mutual information for gait-based soft biometric classification using Gabor features

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  • Author(s): M. Hu 1, 2 ; Y. Wang 1, 2 ; Z. Zhang 1, 2
    • View affiliations
    • Affiliations: 1: State key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China
      2: Laboratory of Intelligent Recognition and Image Processing, School of Computer Science and Engineering, Beihang University, Beijing, China
  • Source: Volume 1, Issue 1, March 2012, p. 55 – 62
    DOI:  10.1049/iet-bmt.2011.0004 , Print ISSN 2047-4938, Online ISSN 2047-4946
© The Institution of Engineering and Technology
Published

Besides identity, soft biometric characteristics, such as gender and age can also be derived from gait patterns. With Gabor enhancement, supervised learning and temporal modelling, the authors present a robust framework to achieve state-of-the-art classification accuracy for both gender and age. Gabor filter and maximisation of mutual information are used to extract low-dimensional features, whereas Bayes rules based on hidden Markov models (HMMs) are adopted for soft biometric classification. The multi-view soft biometric classification problem is defined as two different cases, saying, one-to-one view and many-to-one view, according to the number of available gallery views. In case more than one gallery view is available, the multi-view soft biometric classification problem is hierarchically solved with a view-related population HMM, in which the estimated view angle is treated as the intermediate result in the first stage. Performance has been evaluated on benchmark databases, which verify the advantages of the proposed algorithm.

Inspec keywords: hidden Markov models; image enhancement; gender issues; gait analysis; learning (artificial intelligence); Gabor filters; feature extraction; biometrics (access control); Bayes methods

Other keywords: Bayes rules; supervised learning; Gabor enhancement; HMM; Gabor features; hidden Markov models; temporal modelling; mutual information; gait-based soft biometric classification; gait patterns

Subjects: Neural computing techniques; Computer vision and image processing techniques; Markov processes; Optical, image and video signal processing; Markov processes

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